Predicting the potential for severe weather

ABSTRACT

Methods and apparatuses, including computer program products, are described for predicting the potential for severe weather. A computing device receives data associated with lightning activity, where the data includes lightning flash data. The computing device identifies cells of lighting activity based upon the lightning flash data and determines a movement speed, a movement direction, and a lightning rate of the cells of lightning activity based on the received data. The computing device compares the determined lightning rate to a threshold lightning rate, where the threshold rate is set based upon atmospheric conditions in a location of the cells of lightning activity. The computing device determines one or more geographical areas at risk based on the location, the movement speed, and the movement direction of the cells of lightning activity, and issues an alert to remote devices when the lightning rate exceeds a value of the threshold lightning rate.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/177,266, filed on Jul. 6, 2011, which is incorporated herein in itsentirety.

FIELD OF THE INVENTION

The subject matter of this application relates generally to methods andapparatuses, including computer program products, for predicting thepotential for severe weather.

BACKGROUND

Lightning includes electrical discharges within a cloud, intracloud (IC)discharges, and cloud to ground (CG) discharges. Lightning occurs whenelectrical fields within a cloud intensify as particles of oppositepolarity collect at differing regions within the cloud. Lightning beginswith an initial electrical breakdown (i.e., a pulse) followed by leaderchannels from which a series of channel branches grow within a cloudforming a comprehensive branch channel structure. For IC lightning, thechannel structure remains within the cloud. A CG discharge occurs whenone or more branches extend from a cloud to the ground.

An increase in lightning activity often precedes even more severeweather phenomena, such as severe storms, tornadoes, hail, damagingdownburst winds and potentially deadly cloud-to-ground lightningstrikes. In addition, such lightning activity frequently occurs inlocalized clusters, also called cells. Lightning cells exhibit certaincharacteristics (e.g., lightning rate, IC/CG ratio) that are indicativeof the potential for severe weather. Also, using detection methods andsystems, data associated with lightning cells can be obtained andanalyzed to determine the location and movement of specific cells acrossa geographic region.

Accurate and efficient detection of early lightning activity, such asthe weaker, initial IC discharges, is critical for advanced forecastingof severe weather phenomena. Integrated detection of both IC lightningand CG lightning provides highly advanced predictive capabilities forcharacterizing severe storm precursors, improving lead times andcomprehensive weather management planning. Numerous lightning detectionsystems and methods have been developed, each striving to determine thelocation, movement, frequency and intensity of lightning activity withbetter accuracy. Examples of such systems include the U.S. PrecisionLightning Network (USPLN), the National Lightning Detection Network(NLDN) and the WeatherBug Total Lightning Network (WTLN).

Previous weather alert systems have relied on human intervention todetermine the extent of severe weather activity and to initiate thenotification of remote devices configured to receive alerts (e.g.,through use of a display where a person evaluates weather data andselect devices to receive the alerts). To increase the speed andaccuracy of weather alert systems, it is desirable to eliminate the needfor manual processing of severe weather data and issuance of alertmessages.

SUMMARY

An important objective associated with accurate, advanced detection ofsevere weather activity is the timely issuance of automated warnings oralerts to entities that may be affected by the severe weather. Moreexact detection of atmospheric conditions that potentially result insevere weather, such as lightning rates, leads to a more comprehensiveunderstanding of the risk for dangerous weather activity in a particulargeographic area. Knowledge of severe weather potential before thatsevere weather impacts a particular region allows for greater lead timefor alerts to persons or entities situated in proximity to the at-riskareas, resulting in increased safety for those persons and entities.

In general overview, the techniques described herein are related topredicting the potential for severe weather. The techniquesadvantageously provide automated severe storm prediction for the timelyissuance of reliable severe weather alerts. The techniques utilizeprecise detection of lightning events such as CG and IC lightningflashes to identify the boundaries of lightning cells. The techniquesalso account for differences in geographic location and atmosphericconditions to produce more accurate predictions of the track and timingof severe weather. The techniques further provide for automaticidentification of remote devices configured to receive alerts for aparticular geographic area and automatic transmission of relevant alertsto the remote devices.

The invention, in one aspect, features a computer-implemented method forpredicting the potential for severe weather. A computing device receivesdata associated with lightning activity, where the data includeslightning flash data collected during a specific time interval. Thecomputing device identifies one or more cells of lighting activity basedupon the lightning flash data. The computing device determines amovement speed, a movement direction, and a lightning rate of the one ormore cells of lightning activity based on the received data. Thecomputing device compares the determined lightning rate to a thresholdlightning rate, where the threshold lightning rate is set based uponatmospheric conditions in a location of the one or more cells oflightning activity. The computing device determines one or moregeographical areas at risk based on the location, the movement speed,and the movement direction of the one or more cells of lightningactivity. The computing device issues an alert to one or more remotedevices monitoring the geographical areas at risk when the lightningrate exceeds a value of the threshold lightning rate.

The invention, in another aspect, features a system for predicting thepotential for severe weather. The system includes a computing deviceconfigured to receive data associated with lightning activity, where thedata includes lightning flash data collected during a specific timeinterval. The computing device is configured to identify one or morecells of lighting activity based upon the lightning flash data anddetermine a movement speed, a movement direction, and a lightning rateof the one or more cells of lightning activity based on the receiveddata. The computing device is configured to compare the determinedlightning rate to a threshold lightning rate, where the thresholdlightning rate is set based upon atmospheric conditions in a location ofthe one or more cells of lightning activity. The computing device isconfigured to determine one or more geographical areas at risk based onthe location, the movement speed, and the movement direction of the oneor more cells of lightning activity, and issue an alert to one or moreremote devices monitoring the geographical areas at risk when thelightning rate exceeds a value of the threshold lightning rate.

The invention, in another aspect, features a computer program product,tangibly embodied in a non-transitory computer readable storage device,for predicting the potential for severe weather. The computer programproduct includes instructions operable to cause a computing device toreceive data associated with lightning activity, wherein the dataincludes lightning flash data collected during a specific time interval.The computer program product includes instructions operable to cause thecomputing device to identify one or more cells of lighting activitybased upon the lightning flash data and determine a movement speed, amovement direction, and a lightning rate of the one or more cells oflightning activity based on the received data. The computer programproduct includes instructions operable to cause the computing device tocompare the determined lightning rate to a threshold lightning rate,where the threshold lightning rate is set based upon atmosphericconditions in a location of the one or more cells of lightning activity.The computer program product includes instructions operable to cause thecomputing device to determine one or more geographical areas at riskbased on the location, the movement speed, and the movement direction ofthe one or more cells of lightning activity, and issue an alert to oneor more remote devices monitoring the geographical areas at risk whenthe lightning rate exceeds a value of the threshold lightning rate.

The invention, in another aspect, features a computer-implemented methodfor predicting the potential for severe weather. A computing devicereceives data associated with lightning activity, where the dataincludes lightning flash data collected during a specific time interval.The computing device identifies one or more cells of lighting activitybased upon the lightning flash data and determines a movement speed, amovement direction, and a lightning rate of the one or more cells oflightning activity based on the received data. The computing devicecompares the determined lightning rate to a threshold lightning rate anddetermines a probability of severe weather based on lightningcharacteristics including the determined lightning rate. The computingdevice determines one or more geographical areas at risk based on thelocation, the movement speed, and the movement direction of the one ormore cells of lightning activity, and issues an alert to one or moreremote devices monitoring the geographical areas at risk when thelightning rate exceeds a value of the threshold lightning rate.

The invention, in another aspect, features a system for predicting thepotential for severe weather. The system includes a computing deviceconfigured to receive data associated with lightning activity, where thedata includes lightning flash data collected during a specific timeinterval. The computing device is configured to identify one or morecells of lighting activity based upon the lightning flash data anddetermine a movement speed, a movement direction, and a lightning rateof the one or more cells of lightning activity based on the receiveddata. The computing device is configured to compare the determinedlightning rate to a threshold lightning rate and determine a probabilityof severe weather based on lightning characteristics including thedetermined lightning rate. The computing device is configured todetermine one or more geographical areas at risk based on the location,the movement speed, and the movement direction of the one or more cellsof lightning activity, and issue an alert to one or more remote devicesmonitoring the geographical areas at risk when the lightning rateexceeds a value of the threshold lightning rate.

The invention, in another aspect, features a computer program product,tangibly embodied in a non-transitory computer readable storage device,for predicting the potential for severe weather. The computer programproduct includes instructions operable to cause a computing device toreceive data associated with lightning activity, where the data includeslightning flash data collected during a specific time interval. Thecomputer program product includes instructions operable to cause thecomputing device to identify one or more cells of lighting activitybased upon the lightning flash data and determine a movement speed, amovement direction, and a lightning rate of the one or more cells oflightning activity based on the received data. The computer programproduct includes instructions operable to cause the computing device tocompare the determined lightning rate to a threshold lightning rate anddetermine a probability of severe weather based on lightningcharacteristics including the determined lightning rate. The computerprogram product includes instructions operable to cause the computingdevice to determine one or more geographical areas at risk based on thelocation, the movement speed, and the movement direction of the one ormore cells of lightning activity, and issue an alert to one or moreremote devices monitoring the geographical areas at risk when thelightning rate exceeds a value of the threshold lightning rate.

Any of the above aspects can include one or more of the followingfeatures. In some embodiments, the atmospheric conditions include atleast one of: a vertical temperature profile, a vertical moistureprofile, a vertical wind profile, and a ground truth severe weatherreport. In some embodiments, the ground truth reports of severe weatherinclude real-time observational evidence of severe weather. In someembodiments, the computing device adjusts the threshold lightning ratebased upon changes to the atmospheric conditions.

In some embodiments, the threshold lightning rate is set based onhistorical evidence associated with a time of year and the thresholdlightning rate is adjusted based upon the atmospheric conditions. Insome embodiments, the lightning rate is determined based on a number oflightning events per minute associated with the one or more cells oflightning activity.

In some embodiments, the step of identifying one or more cells oflighting activity based upon the lightning flash data comprisespositioning each lightning flash on a map according to its geographiclocation, superimposing a first grid on the map and identifying sectorsof the first grid with a high density of lightning flashes,superimposing a second grid on the identified sectors of the map tolocate closed contours associated with a lightning cell, and generatinga convex polygon from each of the closed contours. In some embodiments,one or more polygons are generated corresponding to the geographicalareas at risk. In some embodiments, the generated polygons arepositioned on a map in which at least one of the geographical areas atrisk is located. In some embodiments, the generated polygons aretransmitted to the one or more remote devices as part of the alert.

In some embodiments, the data associated with lightning activity isreceived from one or more geographically dispersed sensor devices. Insome embodiments, the alert is issued before severe weather has reachedat least one of the geographical areas at risk.

In some embodiments, the step of determining a probability of severeweather is based on a ratio of in-cloud lightning activity in thelightning flash data to cloud-to-ground lightning activity in thelightning flash data. In some embodiments, the step of determining aprobability of severe weather is based on total charge moment oflightning in the lightning flash data. In some embodiments, the step ofdetermining a probability of severe weather is based on at least one of:an average amplitude of lightning in the lightning flash data and amedia amplitude of lightning in the lightning flash data. In someembodiments, the step of determining a probability of severe weather isbased on a total charge transfer of lightning in the lightning flashdata. In some embodiments, the total charge transfer of lightningincreases as amplitude and/or duration of lightning in the lightningflash data increases. In some embodiments, the step of determining aprobability of severe weather is based on a ratio of total positiveflashes in the lightning flash data to total negative flashes in thelightning flash data.

Other aspects and advantages of the invention will become apparent fromthe following detailed description, taken in conjunction with theaccompanying drawings, illustrating the principles of the invention byway of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

The advantages of the invention described above, together with furtheradvantages, may be better understood by referring to the followingdescription taken in conjunction with the accompanying drawings. Thedrawings are not necessarily to scale, emphasis instead generally beingplaced upon illustrating the principles of the invention.

FIG. 1 is a block diagram of a system for predicting the potential forsevere weather.

FIG. 2 is a flow diagram of a method for predicting the potential forsevere weather using the system.

FIG. 3 is a diagram depicting the identification of a lightning cell bythe system 100 based on the lightning activity data

FIG. 4 is a graph depicting the total lightning rate of an individuallightning cell in comparison with a lightning threshold rate over aspecific period of time.

FIG. 5 is a diagram depicting the identification by the system of ageographical area at risk of severe weather based on the lightningactivity data.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a system 100 for predicting the potentialfor severe weather. The system 100 includes a data collection module102, a data analysis module 104, an alert generation module 106, agraphics processing module 108, and a data storage module 110. In someembodiments, the components (e.g., 100, 102, 104, 106, 108 and 110) ofthe system 100 reside at the same physical location or may be dispersedto different physical locations. In some embodiments, the components ofthe system 100 are located on the same physical device (e.g., a servercomputing device), or are distributed on different physical devices. Thecomponents of the system 100 communicate, for example, via acommunications network (e.g., WAN, LAN, VLAN).

FIG. 2 is a flow diagram of a method 200 for predicting the potentialfor severe weather using the system 100. The data collection module 102receives (202) data associated with lightning activity. The dataanalysis module 104 determines (204) a location, a movement speed, amovement direction and a lightning rate of one or more cells oflightning activity based on the data received by the data collectionmodule 102. The data analysis module 104 compares (206) the determinedlightning rate to a threshold lightning rate. The alert generationmodule 106 determines (208) one or more geographical areas at risk basedon the location, the movement speed, the movement direction of the oneor more cells of lightning activity. The alert generation module 106issues (210) an alert to one or more remote devices monitoring thegeographical areas at risk when the determined lightning rate exceeds avalue of the threshold lightning rate.

The data collection module 102 provides an interface between externaldata sources (not shown) and the data analysis module 104 of the system100. The data collection module 102 receives data associated withlightning activity from various external data collection and/ormonitoring systems. For example, the data collection module 102 receivesdata from a lightning detection system comprising a plurality ofgeographically-dispersed weather sensors (e.g., the WeatherBug TotalLightning Network (WTLN)). In this example, the data collected by theweather sensors includes analog radio frequency (RF) energy (e.g.,pulses or flashes) at different frequencies, as emitted by a lightningdischarge. Additional detail regarding detection of lightning activityand collection of lightning activity data is found in U.S. patentapplication Ser. No. 12/542,404, titled “Method and Apparatus forDetecting Lightning Activity,” which is incorporated herein in itsentirety. Other sources of lightning activity information include, butare not limited to, governmental agencies and third-party privatecompanies. The data collection module 102 communicates with the variousexternal data systems and sources via standard communications networksand methods.

The data collection module 102 also consolidates lightning activity datareceived from a plurality of external data sources into a formatconducive for processing by the data analysis module 104. For example,each data source to which the data collection module 102 is connectedmay transmit data using a different syntax and/or data structure. Thedata collection module 102 parses the incoming data according to anunderstanding of the source of the data and reformat the data so that itconforms to a syntax or structure acceptable to the data analysis module104. In some embodiments, the external data sources transmit thelightning activity data in a standard format (e.g., XML) to reduce theprocessing required of the data collection module 102. The datacollection module 102 communicates with the data storage module 110 tosave and retrieve data received from external sources in preparation fortransmitting the data to the data analysis module 104. Once the data hasbeen received, the data collection module 102 transmits the data to thedata analysis module 104. In some embodiments, the data collectionmodule 102 transmits a notification to the data analysis module 104 thatthe data has been stored in the data storage module 110 and is ready forprocessing by the data analysis module 104. The notification includes areference indicator (e.g., a database address) of the storage locationof the data within the data storage module 110.

The data analysis module 104 processes the lightning activity datareceived by the data collection module 102 and/or stored in the datastorage module 110 to determine the existence of severe weather risk toone or more geographic regions.

The data analysis module 104 determines the severe weather risk to oneor more geographic regions by tracking the location, movement speed,movement direction and lightning rate of one or more cells of lightningactivity based on the data received by the system 100. A lightning cellis a cluster of flashes with a boundary as a polygon determined by theflash density value for a given time period. The data analysis module104 groups the collected flash data into lightning cells, and the dataanalysis module 104 correlates the cell polygons over a period of timeto determine the movement direction (i.e., track) of the cells. Inaddition, the data analysis module 104 counts the number of flashes in aparticular lightning cell to determine the lightning flash rate (e.g.,flashes per minute). The data analysis module 104 further calculates themovement speed and location of the lightning cells.

FIG. 3 is a diagram depicting the identification of a lightning cell 302by the system 100 based on the lightning activity data. The dataanalysis module 104 receives the lightning flash data from the datacollection module 102 and positions each lightning flash (e.g.,lightning flashes 304) according to its geographic location. The dataanalysis module 104 then analyzes the relative position of the lightningflashes to determine the potential boundaries or contours of specificlightning cells (e.g., cell 302).

In some embodiments, the data analysis module 104 executes a series ofgridding processes to determine the location and contours of a lightningcell 302. The data analysis module 104 uses the lightning flash datacollected during a specific time period (e.g., one minute) and placesthe lightning flashes (e.g., flashes 304) on a map. The data analysismodule 104 then superimposes a coarse grid on the map to quickly locateareas of interest for further analysis. The data analysis module 104identifies the sectors of the grid that contain a high percentage ordensity of lightning flashes and superimposes a fine grid on theidentified sectors. The data analysis module 104 employs densityfunctions on the sectors of the fine grid to locate closed contoursassociated with the lightning cell 302. The data analysis module 104generates a convex polygon (e.g., convex polygon 306) from each of theclosed contours.

The data analysis module 104 repeats this gridding process at theexpiration of a specific time period (e.g., one minute) in order totrack changes in movement, direction and lightning flash rate of thelightning cell 302. In most cases, the polygon 306 generated by the dataanalysis module 104 for a particular lightning cell at each timeinterval is similar to the previously-generated polygon for that cell,so the data analysis module 104 efficiently and quickly correlates thetwo polygons. However, in the case of a sharp increase in the lightningflash rate, lightning cell split or lightning cell merger, thecorrelation of subsequent polygons for a particular cell is not obvious.The data analysis module 104 links the individual lightning cellpolygons based on the dynamically-changing data to produce a path 308 ofthe moving lightning cell. For example, when a lightning cell regroupsafter weakening, based on the trajectory of the cell and thetime-distance of two polygons, the data analysis module 104 maintains acontinuous cell path 308.

The data analysis module 104 also compares the lightning flash ratecalculated from the received lightning activity data to a thresholdlightning rate. The data analysis module 104 also monitors the ratechanges associated with the lightning flash rate of the identifiedlightning cells. By monitoring the flash rates and the rate changes,severe storm cells (and cells that potentially will become severe) areidentified and tracked. The threshold lightning rate used by the dataanalysis module 104 is relevant to the probability that the trackedlightning cell is associated with severe weather and may be used by thesystem 100 to determine when to issue an alert. For example, if thelightning rate exceeds the threshold rate, the possibility that thelightning cell is associated with severe weather is sufficient towarrant the issuance of an alert.

FIG. 4 is a graph 400 depicting the total lightning rate 402 of anindividual lightning cell (e.g., cell 302 of FIG. 3) in comparison witha lightning threshold rate 404 over a specific period of time. The dataanalysis module 104 determines the total lightning rate 402 of alightning cell by analyzing the number of lightning events (e.g.,flashes) in a specific time interval (e.g., one minute). In someembodiments, lightning events include both CG and IC lightning. In someembodiments the data analysis module 104 evaluates the collectedlightning data to identify various types of IC lightning, including airdischarges, intracloud flashes, and/or cloud-to-ionosphere flashes.

By continuously calculating the total lightning rate of a particularlightning cell at regular time intervals, the data analysis module 104detects changes in the total lightning rate between the time intervals.Based on this approach, the data analysis module 104 determines ifchanges in the lightning rate have occurred that may be indicative ofsevere weather generally or a specific type of severe weather (e.g.,precipitation, wind events). For example, the total lightning rate 402in FIG. 4 begins to increase sharply starting at time=t(0) and peakingat time=t(p), with the severe weather associated with the lightning celloccurring at time=t(s). The data analysis module 104 determines that thetotal lightning rate 402 meets the threshold lightning rate 304 attime=t(i) and transmits information to the alert generation module 106.In addition, the data analysis module 104 compares the changes in ratebetween time=t(0), time=t(p) and time=t(s) against a database ofhistorical lightning rate activity to identify similarities or patternsin the lightning rate change. As an example, the specific rate changesdepicted in FIG. 4 may occur multiple times during the life of alightning cell, and the severe weather resulting at time=t(s) may be theonset of an intense hail storm. As a result, the data analysis module104 instructs the alert generation module 106 to provide a more detailedalert message based on this additional information.

The data analysis module 104 also uses the historical data to set athreshold rate for a specific lightning cell. For example, the dataanalysis module 104 determines the threshold lightning rate by using abest-fit analysis method based on review of actual weather data. In someembodiments, the historical data is associated with a particular time ofyear and/or a particular geographic region. Based on a correlationbetween the historical time of year and the time of year during whichthe current lightning cell is being tracked, the data analysis module104 adjusts the threshold rate to account for similarities ordifferences between the two data points. For example, if a lightningcell is being tracked during a time of year that has exhibitedtraditionally low occurrence of severe weather, the data analysis module104 moves the threshold rate up to require a higher total lightning ratebefore an alert is issued by the system 100. Conversely, if a lightningcell is being tracked during a time of year that has been prone toincreased severe weather activity, the data analysis module 104 movesthe threshold rate down to require a lower total lightning rate beforeissuance of an alert.

Once the data analysis module 104 determines that the total lightningrate of the currently-tracked lightning cell (e.g., cell 302 of FIG. 3)has exceeded the threshold lightning rate (i.e., is associated with asufficient potential for severe weather), the data analysis module 104transmits data to the alert generation module 106. The alert generationmodule 106 uses the analyzed characteristics of the lightning cell toautomatically determine geographical areas that may be impacted by thelightning cell as it moves and changes in size and/or intensity.

FIG. 5 is a diagram 500 depicting the identification by the system 100of a geographical area at risk 502 of severe weather based on thelightning activity data. In order to issue an alert that reaches personsand/or entities that may be directly affected by the severe weather orthat may have an interest in the affected area, the alert generationmodule 106 determines one or more geographical areas at risk 502 basedon the location, movement speed and movement direction of the lightningcell 302. In some embodiments, the alert generation module 106determines a warning area that corresponds to the current location andexpected track of the cell during an upcoming period of time. Forexample, the alert generation module 106 generates a polygon 502 thatcovers the range of distances and directions that a lightning cell couldtravel in a specific period of time (e.g., forty-five minutes) byevaluating the movement speed and the movement direction of the celldemonstrated at the time the data analysis module 104 determines thatthe total lightning rate of the cell 302 exceeded the thresholdlightning rate.

After receiving notification from the data analysis module 104 anddetermining one or more areas at risk, the alert generation module 106automatically identifies a set of one or more remote devices that aremonitoring the at-risk areas and automatically transmits an alert to theremote devices. The remote devices can include computer-based devices,such as mobile phones and global positioning system (GPS) hardware. Theremote devices can also include other types of warning systems, such aslights, sirens and horns that are configured to connect to acommunications network. In some embodiments, the data storage device 110includes information related to identification of the remote devices(e.g., IP address, phone number, email address), and the alertgeneration module 108 uses the identification information to prepare analert for each remote device. The data storage device 110 also includesinformation mapping the identification of a remote device to aparticular geographic area or areas that the remote device is monitoring(e.g., zip code, county name, street address). The alert generationmodule 106 uses any standard communication protocol or technique, suchas packet-based delivery (e.g., text messaging, XML, email),circuit-based delivery (e.g., paging, voice messaging), and the like.For example, a user can subscribe to receive alerts for a particular zipcode on his mobile phone. The system 100 stores the user's telephonenumber in the data storage module 110. When the alert generation module106 identifies a geographic location that is at risk for severe weatherand all or part of the identified location falls within the zip codesubmitted by the user, the alert generation module 108 issues an alert(e.g., a text message, a voice message) addressed to the telephonenumber of the user's mobile phone. In this embodiment, the user's mobilephone need not be located in the same geographic area as identified bythe alert generation module 106 as “at risk.”

In some embodiments, the alert is further enhanced by the inclusion of agraphical representation of the geographical area that is under thethreat of severe weather. The graphical representation provides anadditional, easily recognizable piece of information for the recipientof the alert. For example, the alert generation module 106 superimposesa polygon 502 delineating the geographical area at risk associated witha specific lightning cell 302 on a map. The alert generation module 106utilizes a graphics processing module 108 to generate a visualrepresentation of the polygon 502 representing the at-risk area asplaced on the map. In some embodiments, the graphics processing module108 is a separate graphics processing unit (GPU) (e.g., a graphics card)or a software module configured to produce graphic drawings and designsbased on the lighting activity data.

The above-described techniques can be implemented in digital and/oranalog electronic circuitry, or in computer hardware, firmware,software, or in combinations of them. The implementation can be as acomputer program product, i.e., a computer program tangibly embodied ina machine-readable storage device, for execution by, or to control theoperation of, a data processing apparatus, e.g., a programmableprocessor, a computer, and/or multiple computers. A computer program canbe written in any form of computer or programming language, includingsource code, compiled code, interpreted code and/or machine code, andthe computer program can be deployed in any form, including as astand-alone program or as a subroutine, element, or other unit suitablefor use in a computing environment. A computer program can be deployedto be executed on one computer or on multiple computers at one or moresites.

Method steps can be performed by one or more processors executing acomputer program to perform functions of the invention by operating oninput data and/or generating output data. Method steps can also beperformed by, and an apparatus can be implemented as, special purposelogic circuitry, e.g., a FPGA (field programmable gate array), a FPAA(field-programmable analog array), a CPLD (complex programmable logicdevice), a PSoC (Programmable System-on-Chip), ASIP(application-specific instruction-set processor), or an ASIC(application-specific integrated circuit), or the like. Subroutines canrefer to portions of the stored computer program and/or the processor,and/or the special circuitry that implement one or more functions.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital or analog computer.Generally, a processor receives instructions and data from a read-onlymemory or a random access memory or both. The essential elements of acomputer are a processor for executing instructions and one or morememory devices for storing instructions and/or data. Memory devices,such as a cache, can be used to temporarily store data. Memory devicescan also be used for long-term data storage. Generally, a computer alsoincludes, or is operatively coupled to receive data from or transferdata to, or both, one or more mass storage devices for storing data,e.g., magnetic, magneto-optical disks, or optical disks. A computer canalso be operatively coupled to a communications network in order toreceive instructions and/or data from the network and/or to transferinstructions and/or data to the network. Computer-readable storagemediums suitable for embodying computer program instructions and datainclude all forms of volatile and non-volatile memory, including by wayof example semiconductor memory devices, e.g., DRAM, SRAM, EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and optical disks,e.g., CD, DVD, HD-DVD, and Blu-ray disks. The processor and the memorycan be supplemented by and/or incorporated in special purpose logiccircuitry.

To provide for interaction with a user, the above described techniquescan be implemented on a computer in communication with a display device,e.g., a CRT (cathode ray tube), plasma, or LCD (liquid crystal display)monitor, for displaying information to the user and a keyboard and apointing device, e.g., a mouse, a trackball, a touchpad, or a motionsensor, by which the user can provide input to the computer (e.g.,interact with a user interface element). Other kinds of devices can beused to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, and/ortactile input.

The above described techniques can be implemented in a distributedcomputing system that includes a back-end component. The back-endcomponent can, for example, be a data server, a middleware component,and/or an application server. The above described techniques can beimplemented in a distributed computing system that includes a front-endcomponent. The front-end component can, for example, be a clientcomputer having a graphical user interface, a Web browser through whicha user can interact with an example implementation, and/or othergraphical user interfaces for a transmitting device. The above describedtechniques can be implemented in a distributed computing system thatincludes any combination of such back-end, middleware, or front-endcomponents.

The components of the computing system can be interconnected bytransmission medium, which can include any form or medium of digital oranalog data communication (e.g., a communication network). Transmissionmedium can include one or more packet-based networks and/or one or morecircuit-based networks in any configuration. Packet-based networks caninclude, for example, the Internet, a carrier internet protocol (IP)network (e.g., local area network (LAN), wide area network (WAN), campusarea network (CAN), metropolitan area network (MAN), home area network(HAN)), a private IP network, an IP private branch exchange (IPBX), awireless network (e.g., radio access network (RAN), Bluetooth, Wi-Fi,WiMAX, general packet radio service (GPRS) network, HiperLAN), and/orother packet-based networks. Circuit-based networks can include, forexample, the public switched telephone network (PSTN), a legacy privatebranch exchange (PBX), a wireless network (e.g., RAN, code-divisionmultiple access (CDMA) network, time division multiple access (TDMA)network, global system for mobile communications (GSM) network), and/orother circuit-based networks.

Information transfer over transmission medium can be based on one ormore communication protocols. Communication protocols can include, forexample, Ethernet protocol, Internet Protocol (IP), Voice over IP(VOIP), a Peer-to-Peer (P2P) protocol, Hypertext Transfer Protocol(HTTP), Session Initiation Protocol (SIP), H.323, Media Gateway ControlProtocol (MGCP), Signaling System #7 (SS7), a Global System for MobileCommunications (GSM) protocol, a Push-to-Talk (PTT) protocol, a PTT overCellular (POC) protocol, and/or other communication protocols.

Devices of the computing system can include, for example, a computer, acomputer with a browser device, a telephone, an IP phone, a mobiledevice (e.g., cellular phone, personal digital assistant (PDA) device,laptop computer, electronic mail device), and/or other communicationdevices. The browser device includes, for example, a computer (e.g.,desktop computer, laptop computer) with a World Wide Web browser (e.g.,Microsoft® Internet Explorer® available from Microsoft Corporation,Mozilla® Firefox available from Mozilla Corporation). Mobile computingdevice include, for example, a Blackberry®. IP phones include, forexample, a Cisco® Unified IP Phone 7985G available from Cisco Systems,Inc, and/or a Cisco® Unified Wireless Phone 7920 available from CiscoSystems, Inc.

Comprise, include, and/or plural forms of each are open ended andinclude the listed parts and can include additional parts that are notlisted. And/or is open ended and includes one or more of the listedparts and combinations of the listed parts.

One skilled in the art will realize the invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. The foregoing embodiments are therefore to beconsidered in all respects illustrative rather than limiting of theinvention described herein.

What is claimed is:
 1. A computer-implemented method for predicting thepotential for severe weather, the method comprising: receiving, by acomputing device, data associated with lightning activity, wherein thedata includes lightning flash data collected during a specific timeinterval; identifying, by the computing device, one or more cells oflighting activity based upon the lightning flash data, comprising:positioning each lightning flash on a map according to its geographiclocation; superimposing a first grid on the map and identifying sectorsof the first grid with a high density of lightning flashes;superimposing a second grid on the identified sectors of the map tolocate closed contours associated with a lightning cell; and generatinga convex polygon from each of the closed contours; determining, by thecomputing device, a movement speed, a movement direction, and alightning rate of the one or more cells of lightning activity based onthe received data; generating, by the computing device, a thresholdlightning rate for each of the one or more cells of lightning activityby correlating present atmospheric conditions in a location of the oneor more cells of lightning activity with historical atmosphericconditions in the location of the one or more cells of lightningactivity; comparing, by the computing device, the determined lightningrate to the threshold lightning rate; determining, by the computingdevice, one or more geographical areas at risk based on the location,the movement speed, and the movement direction of the one or more cellsof lightning activity; and issuing, by the computing device, an alert toone or more remote devices monitoring the geographical areas at riskwhen the determined lightning rate exceeds the threshold lightning rate.2. The method of claim 1, wherein the present atmospheric conditions andthe historical atmospheric conditions include at least one of: avertical temperature profile, a vertical moisture profile, a verticalwind profile, or a ground truth severe weather report.
 3. The method ofclaim 2, wherein the ground truth reports of severe weather includereal-time observational evidence of severe weather.
 4. The method ofclaim 1, further comprising adjusting, by the computing device, thethreshold lightning rate based upon changes to the present atmosphericconditions.
 5. The method of claim 1, wherein the historical atmosphericconditions are associated with a time of year.
 6. The method of claim 1,wherein the lightning rate is determined based on a number of lightningevents per minute associated with the one or more cells of lightningactivity.
 7. The method of claim 1, further comprising generating, bythe computing device, one or more polygons corresponding to thegeographical areas at risk.
 8. The method of claim 7, further comprisingpositioning, by the computing device, the generated polygons on a map inwhich at least one of the geographical areas at risk is located.
 9. Themethod of claim 7, further comprising transmitting, by the computingdevice, the generated polygons to the one or more remote devices as partof the alert.
 10. The method of claim 1, further comprising receiving,by the computing device, the data associated with lightning activityfrom one or more geographically dispersed sensor devices.
 11. The methodof claim 1, further comprising issuing, by the computing device, thealert before severe weather has reached at least one of the geographicalareas at risk.
 12. A system for predicting the potential for severeweather, the system comprising a computing device configured to: receivedata associated with lightning activity, wherein the data includeslightning flash data collected during a specific time interval; identifyone or more cells of lighting activity based upon the lightning flashdata comprising: positioning each lightning flash on a map according toits geographic location; superimposing a first grid on the map andidentifying sectors of the first grid with a high density of lightningflashes; superimposing a second grid on the identified sectors of themap to locate closed contours associated with a lightning cell; andgenerating a convex polygon from each of the closed contours; determinea movement speed, a movement direction, and a lightning rate of the oneor more cells of lightning activity based on the received data; generatea threshold lightning rate for each of the one or more cells oflightning activity by correlating present atmospheric conditions in alocation of the one or more cells of lightning activity with historicalatmospheric conditions in the location of the one or more cells oflightning activity; compare the determined lightning rate to thethreshold lightning rate; determine one or more geographical areas atrisk based on the location, the movement speed, and the movementdirection of the one or more cells of lightning activity; and issue analert to one or more remote devices monitoring the geographical areas atrisk when the determined lightning rate exceeds the threshold lightningrate.
 13. The system of claim 12, wherein the present atmosphericconditions and the historical atmospheric conditions include at leastone of: a vertical temperature profile, a vertical moisture profile, avertical wind profile, or a ground truth severe weather report.
 14. Thesystem of claim 13, wherein the ground truth reports of severe weatherinclude real-time observational evidence of severe weather.
 15. Thesystem of claim 12, wherein the computing device is further configuredto adjust the threshold lightning rate based upon changes to the presentatmospheric conditions.
 16. The system of claim 12, wherein thehistorical atmospheric conditions are associated with a time of year.17. The system of claim 12, wherein the computing device determines thelightning rate based on a number of lightning events per minuteassociated with the one or more cells of lightning activity.
 18. Thesystem of claim 12, wherein the computing device generates one or morepolygons corresponding to the geographical areas at risk.
 19. The systemof claim 18, wherein the computing device positions the generatedpolygons on a map in which at least one of the geographical areas atrisk is located.
 20. The system of claim 18, wherein the computingdevice transmits the generated polygons to the one or more remotedevices as part of the alert.
 21. The system of claim 12, wherein thecomputing device receives the data associated with lightning activityfrom one or more geographically dispersed sensor devices.
 22. The systemof claim 12, wherein the computing device issues the alert before severeweather has reached at least one of the geographical areas at risk. 23.A computer program product, tangibly embodied in a non-transitorycomputer readable storage device, for predicting the potential forsevere weather, the computer program product including instructionsoperable to cause a computing device to: receive data associated withlightning activity, wherein the data includes lightning flash datacollected during a specific time interval; identify one or more cells oflighting activity based upon the lightning flash data, comprising:positioning each lightning flash on a map according to its geographiclocation; superimposing a first grid on the map and identifying sectorsof the first grid with a high density of lightning flashes;superimposing a second grid on the identified sectors of the map tolocate closed contours associated with a lightning cell; and generatinga convex polygon from each of the closed contours; determine a movementspeed, a movement direction, and a lightning rate of the one or morecells of lightning activity based on the received data; generate athreshold lightning rate for each of the one or more cells of lightningactivity by correlating present atmospheric conditions in a location ofthe one or more cells of lightning activity with historical atmosphericconditions in the location of the one or more cells of lightningactivity; compare the determined lightning rate to the thresholdlightning rate; determine one or more geographical areas at risk basedon the location, the movement speed, and the movement direction of theone or more cells of lightning activity; and issue an alert to one ormore remote devices monitoring the geographical areas at risk when thedetermined lightning rate exceeds the threshold lightning rate.